Bridging the Gap: Integrating All Enterprise Data for a Smarter Future

Rebecca Dilthey
Rocket Software

Businesses are facing a critical challenge: how to leverage their complete data ecosystem to drive growth and competitive advantage. Integrating their mainframe data with hybrid cloud data is still the biggest hurdle. As organizations strive to become more agile and data-driven to maintain their competitiveness, this integration is becoming increasingly essential, especially since critical customer data still resides in these transactional systems. Alarmingly, 64% of IT leaders report struggling to deliver mainframe data in real-time, preventing the business from realizing the full potential of their data.

Despite widespread recognition of the strategic value of mainframe data – which includes transaction records, customer information, and inventory management – many businesses still lack the tools and strategies to unlock their potential. A recent Rocket Software and Foundry study found that just 28% of organizations fully leverage their mainframe data, a concerning statistic given its critical role in powering AI models, predictive analytics, and informed decision-making.

So, what's holding companies back, and how can they overcome this challenge to unlock the full power of their data?

The Roadblock: Data Silos and Integration Challenges

Mainframe systems have long been the backbone of many businesses, providing security, scale, reliability, and performance that modern systems can't match. However, while they excel in their core functionalities, they don't integrate natively with modern hybrid cloud technologies. As a result, many organizations find themselves with data silos, where valuable insights are trapped within core systems and disconnected from hybrid cloud-based analytics and applications.

This challenge is further complicated by stringent data governance requirements, security concerns, and the need for specialized expertise to manage and integrate these systems. According to the Foundry report, 76% of companies find that applying governance to mainframe data is difficult. Without a seamless connection between mainframe and hybrid cloud data, AI and machine learning models may rely on incomplete or outdated information, which can reduce their accuracy and effectiveness. Additionally, KPMG reports that 94% of businesses believe the data they collect and store is not completely accurate.

The outcome? Businesses miss out on critical insights that could drive more intelligent decision-making and give them a competitive edge.

Although each system works well independently, their true potential is unlocked when their data is integrated. Bridging this gap enables businesses to enhance real-time decision-making, improve efficiency, and achieve a new level of operational agility.

Bridging Mainframe and Hybrid Cloud with Intelligent Data Integration

To bridge the gap between mainframe and hybrid cloud environments, businesses need a modern, flexible, technology-driven strategy — one that ensures they can access, analyze, and act on their data without disruption. Rather than relying on costly, high-risk "rip-and-replace" modernization efforts, organizations can integrate their core transactional data with modern cloud platforms using automated, secure, and scalable solutions capable of understanding and modernizing mainframe data.

One of the most effective methods is real-time data replication and synchronization, which enables mainframe data to be continuously updated in hybrid cloud environments in real time. Low-impact change data capture technology recognizes and replicates only the modified portions of datasets, reducing processing overhead and ensuring real-time consistency across both mainframe and hybrid cloud systems.

Another approach is API-based integration, which allows organizations to provide mainframe data as modern, cloud-compatible services. This eliminates the need for batch processing and enables cloud-native applications, AI models, and analytics platforms to access real-time mainframe data on demand. API gateways further enhance security and governance, ensuring only authorized systems can interact with sensitive transactional business data.

Metadata-driven automation is arguably the most effective data integration method. Since all data has associated metadata, it's the common denominator that can eliminate data silos, playing a key role in simplifying integration. These solutions automatically discover, classify, and map mainframe datasets to hybrid cloud environments, reducing the need for manual effort and accelerating migration. When combined with high-performance data virtualization and real-time data replication and synchronization, businesses can get a unified view of their enterprise data while preserving system performance and security.

Implementing modern integration strategies transforms mainframe data into an accessible, real-time resource for AI-driven decision-making, predictive analytics, and business intelligence. This shift enables organizations to become truly "data-driven." According to a McKinsey Global Institute report, data-driven organizations are 23 times more likely to acquire customers, six times more likely to retain them, and 19 times more likely to be profitable. With the right solutions in place, businesses no longer need to choose between the reliability of legacy systems and the innovation of hybrid cloud; they can combine the advantages of both to enhance their performance.

Key Benefits of Bridging the Mainframe-Hybrid Cloud Divide

Real-Time Data Synchronization

One of the biggest challenges in integrating mainframe data with cloud environments is connecting disparate systems in real-time. Advanced integration tools allow organizations to easily synchronize data across on-premises systems, mainframes, distributed, and cloud applications. This ensures that critical information is always up-to-date and accessible, enabling businesses to respond faster to market changes and operational demands.

Faster Access to Data Insights

Traditionally, accessing and analyzing mainframe data has been slow and cumbersome. The key to unlocking value lies in automating data scanning and mapping from across the enterprise. With the right integration solution, businesses can quickly transform raw data into meaningful insights, supporting better decision-making and more accurate forecasting.

Cost Efficiency and Increased Agility

Hybrid cloud infrastructures offer significant cost savings over traditional data management approaches. By integrating mainframe data with hybrid cloud environments, businesses can reduce operational costs, optimize resource use, and improve business agility. This allows organizations to scale their data management capabilities more efficiently, enabling faster delivery of services and innovations while minimizing unnecessary overhead.

Improved Workflow and Operational Efficiency

Integrating mainframe and hybrid cloud data improves overall workflow efficiency by minimizing data silos, reducing complexity, and eliminating compatibility issues. When data is seamlessly unified across systems, businesses can streamline their operations, cut down on development delays, and improve employee productivity. This translates into more efficient use of resources, better collaboration across teams, and fewer errors or disruptions in business operations.

Embracing a Hybrid Cloud Strategy to Support AI-Driven Initiatives

Integrating data across the enterprise is crucial for businesses to fully embrace AI-driven decision-making. Hybrid cloud platforms provide the ideal environment to realize the full potential of their mainframe data, enabling faster analytics, real-time insights, and greater business agility.

The integration process doesn't have to be overwhelming. Modern tools that provide automated data discovery, metadata management, and seamless integration can simplify the connection between mainframe systems and cloud infrastructures. The best solutions are also flexible enough to adapt to an organization's current needs, helping to address immediate challenges while gradually building out capabilities based on the company's priorities. These technologies enable businesses to synchronize data effortlessly, manage it securely across different environments, and eliminate governance issues — all while reducing risk and operational complexity.

The Future of Data Integration

Data is revenue. It's just that simple. As data drives business transformation, organizations must adopt scalable, flexible, and secure solutions to bridge the gap between traditional and modern systems. By unlocking the power of their mainframe data, businesses can fuel AI innovation, improve decision-making, and uncover new revenue opportunities.

Seeing the complete, accurate, and up-to-date picture of the enterprise is a critical enabler of business success. Breaking down data silos, optimizing workflows, and unlocking real-time insights drive more significant innovation, improved efficiency, and more intelligent decision-making – paving the way for a more agile, competitive, and data-driven future.

Rebecca Dilthey is a Product Marketing Director at Rocket Software

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Bridging the Gap: Integrating All Enterprise Data for a Smarter Future

Rebecca Dilthey
Rocket Software

Businesses are facing a critical challenge: how to leverage their complete data ecosystem to drive growth and competitive advantage. Integrating their mainframe data with hybrid cloud data is still the biggest hurdle. As organizations strive to become more agile and data-driven to maintain their competitiveness, this integration is becoming increasingly essential, especially since critical customer data still resides in these transactional systems. Alarmingly, 64% of IT leaders report struggling to deliver mainframe data in real-time, preventing the business from realizing the full potential of their data.

Despite widespread recognition of the strategic value of mainframe data – which includes transaction records, customer information, and inventory management – many businesses still lack the tools and strategies to unlock their potential. A recent Rocket Software and Foundry study found that just 28% of organizations fully leverage their mainframe data, a concerning statistic given its critical role in powering AI models, predictive analytics, and informed decision-making.

So, what's holding companies back, and how can they overcome this challenge to unlock the full power of their data?

The Roadblock: Data Silos and Integration Challenges

Mainframe systems have long been the backbone of many businesses, providing security, scale, reliability, and performance that modern systems can't match. However, while they excel in their core functionalities, they don't integrate natively with modern hybrid cloud technologies. As a result, many organizations find themselves with data silos, where valuable insights are trapped within core systems and disconnected from hybrid cloud-based analytics and applications.

This challenge is further complicated by stringent data governance requirements, security concerns, and the need for specialized expertise to manage and integrate these systems. According to the Foundry report, 76% of companies find that applying governance to mainframe data is difficult. Without a seamless connection between mainframe and hybrid cloud data, AI and machine learning models may rely on incomplete or outdated information, which can reduce their accuracy and effectiveness. Additionally, KPMG reports that 94% of businesses believe the data they collect and store is not completely accurate.

The outcome? Businesses miss out on critical insights that could drive more intelligent decision-making and give them a competitive edge.

Although each system works well independently, their true potential is unlocked when their data is integrated. Bridging this gap enables businesses to enhance real-time decision-making, improve efficiency, and achieve a new level of operational agility.

Bridging Mainframe and Hybrid Cloud with Intelligent Data Integration

To bridge the gap between mainframe and hybrid cloud environments, businesses need a modern, flexible, technology-driven strategy — one that ensures they can access, analyze, and act on their data without disruption. Rather than relying on costly, high-risk "rip-and-replace" modernization efforts, organizations can integrate their core transactional data with modern cloud platforms using automated, secure, and scalable solutions capable of understanding and modernizing mainframe data.

One of the most effective methods is real-time data replication and synchronization, which enables mainframe data to be continuously updated in hybrid cloud environments in real time. Low-impact change data capture technology recognizes and replicates only the modified portions of datasets, reducing processing overhead and ensuring real-time consistency across both mainframe and hybrid cloud systems.

Another approach is API-based integration, which allows organizations to provide mainframe data as modern, cloud-compatible services. This eliminates the need for batch processing and enables cloud-native applications, AI models, and analytics platforms to access real-time mainframe data on demand. API gateways further enhance security and governance, ensuring only authorized systems can interact with sensitive transactional business data.

Metadata-driven automation is arguably the most effective data integration method. Since all data has associated metadata, it's the common denominator that can eliminate data silos, playing a key role in simplifying integration. These solutions automatically discover, classify, and map mainframe datasets to hybrid cloud environments, reducing the need for manual effort and accelerating migration. When combined with high-performance data virtualization and real-time data replication and synchronization, businesses can get a unified view of their enterprise data while preserving system performance and security.

Implementing modern integration strategies transforms mainframe data into an accessible, real-time resource for AI-driven decision-making, predictive analytics, and business intelligence. This shift enables organizations to become truly "data-driven." According to a McKinsey Global Institute report, data-driven organizations are 23 times more likely to acquire customers, six times more likely to retain them, and 19 times more likely to be profitable. With the right solutions in place, businesses no longer need to choose between the reliability of legacy systems and the innovation of hybrid cloud; they can combine the advantages of both to enhance their performance.

Key Benefits of Bridging the Mainframe-Hybrid Cloud Divide

Real-Time Data Synchronization

One of the biggest challenges in integrating mainframe data with cloud environments is connecting disparate systems in real-time. Advanced integration tools allow organizations to easily synchronize data across on-premises systems, mainframes, distributed, and cloud applications. This ensures that critical information is always up-to-date and accessible, enabling businesses to respond faster to market changes and operational demands.

Faster Access to Data Insights

Traditionally, accessing and analyzing mainframe data has been slow and cumbersome. The key to unlocking value lies in automating data scanning and mapping from across the enterprise. With the right integration solution, businesses can quickly transform raw data into meaningful insights, supporting better decision-making and more accurate forecasting.

Cost Efficiency and Increased Agility

Hybrid cloud infrastructures offer significant cost savings over traditional data management approaches. By integrating mainframe data with hybrid cloud environments, businesses can reduce operational costs, optimize resource use, and improve business agility. This allows organizations to scale their data management capabilities more efficiently, enabling faster delivery of services and innovations while minimizing unnecessary overhead.

Improved Workflow and Operational Efficiency

Integrating mainframe and hybrid cloud data improves overall workflow efficiency by minimizing data silos, reducing complexity, and eliminating compatibility issues. When data is seamlessly unified across systems, businesses can streamline their operations, cut down on development delays, and improve employee productivity. This translates into more efficient use of resources, better collaboration across teams, and fewer errors or disruptions in business operations.

Embracing a Hybrid Cloud Strategy to Support AI-Driven Initiatives

Integrating data across the enterprise is crucial for businesses to fully embrace AI-driven decision-making. Hybrid cloud platforms provide the ideal environment to realize the full potential of their mainframe data, enabling faster analytics, real-time insights, and greater business agility.

The integration process doesn't have to be overwhelming. Modern tools that provide automated data discovery, metadata management, and seamless integration can simplify the connection between mainframe systems and cloud infrastructures. The best solutions are also flexible enough to adapt to an organization's current needs, helping to address immediate challenges while gradually building out capabilities based on the company's priorities. These technologies enable businesses to synchronize data effortlessly, manage it securely across different environments, and eliminate governance issues — all while reducing risk and operational complexity.

The Future of Data Integration

Data is revenue. It's just that simple. As data drives business transformation, organizations must adopt scalable, flexible, and secure solutions to bridge the gap between traditional and modern systems. By unlocking the power of their mainframe data, businesses can fuel AI innovation, improve decision-making, and uncover new revenue opportunities.

Seeing the complete, accurate, and up-to-date picture of the enterprise is a critical enabler of business success. Breaking down data silos, optimizing workflows, and unlocking real-time insights drive more significant innovation, improved efficiency, and more intelligent decision-making – paving the way for a more agile, competitive, and data-driven future.

Rebecca Dilthey is a Product Marketing Director at Rocket Software

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Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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